Cell_cycle_alport_proteomics
Geremy Clair
December 17, 2020
Required libraries
The required libraries are loaded - RomicsProcessor written by Geremy Clair (2020) is used to perform trackable transformation and statistics to the dataset - proteinminion written by Geremy Clair (2020) is used to extract fasta information and to perform gene ontology and KEGG pathways enrichement analysis (in prep 2020)
library("RomicsProcessor")
library("proteinminion")
library("eulerr") #for the venn and euler diagramsFasta and protein ontologies download using ‘Protein Mini-On’
Using the package ‘Protein Mini-on’ (Geremy Clair 2020, in prep.), The fasta file was downloaded from Uniprot.
if(!file.exists("./03_Output_files/Mus_musculus_proteome_up000000589_2020_07_13.fasta")){
download_UniProtFasta(proteomeID = "up000000589", reviewed = F, export=T, file="./03_Output_files/Mus_musculus_proteome_up000000589_2020_07_13.fasta")
}Then we’ve extracted and parsed the details contained in the fasta file header into a table containing a list of details for each protein.
if(!file.exists("./03_Output_files/UniProt_Fasta_info.csv")){
write.csv(UniprotFastaParser(file = "./03_Output_files/Mus_musculus_proteome_up000000589_2020_07_13.fasta"),file="./03_Output_files/UniProt_Fasta_info.csv")
}For each entry, ‘Protein Mini-On’ was use to download Gene Ontology (GO) terms and KEGG ids associated with the proteins. This upload was performed the exact same day as the download of the fasta file was done to ensure that the IDs will be identical as the ones present in the fasta file used).
if(file.exists("./03_Output_files/UniprotTable_Mus_musculus_proteome_up000000589_2020_07_13.csv")){
UniProtTable<-read.csv("./03_Output_files/UniprotTable_Mus_musculus_proteome_up000000589_2020_07_13.csv")
}else{
download_UniProtTable(proteomeID = "up000000589", reviewed = F)
write.csv(UniProtTable,("./03_Output_files/UniprotTable_Mus_musculus_proteome_up000000589_2020_07_13.csv"),row.names=FALSE)
}‘Protein-Mini-on’ was then used to generate a table (UniProtTable) containing the list of GOs and their associated protein IDs
if(file.exists("./03_Output_files/UniProtTable_GO.csv")){
UniProtTable_GO<-read.csv(file="./03_Output_files/UniProtTable_GO.csv")
}else{
generate_UniProtTable_GO()
write.csv(UniProtTable_GO,file="./03_Output_files/UniProtTable_GO.csv",row.names=FALSE)
}‘Protein-Mini-on’ was used to download similar information from KEGG for the Pathways associated with each protein
if(file.exists("./03_Output_files/UniProtTable_KEGG.csv")){
UniProtTable_KEGG<-read.csv(file="./03_Output_files/UniProtTable_KEGG.csv")
}else{
generate_UniProtTable_KEGG()
write.csv(UniProtTable_KEGG,file="./03_Output_files/UniProtTable_KEGG.csv",row.names=FALSE)
}MaxQuant import
The data was searched in MaxQuant using the mouse database generated above using the LFQ quantification and Match Beetwen Runs (MBR) algorithm, the parameter.txt file indicates the parameters employed.
data<-extractMaxQuant("./01_Source_files/proteinGroups.txt",quantification_type = "iBAQ",cont.rm = T,site.rm = T,rev.rm = T)## [1] "70 Proteins were removed (protein(s) only identified by site,contaminant(s),reverse hit(s))"
## [1] "iBAQ quantification was used"write.csv(data,file="./03_Output_files/data_raw.csv")
IDsdetails<-extractMaxQuantIDs("./01_Source_files/proteinGroups.txt",cont.rm = T,site.rm = T,rev.rm = T)## [1] "70 Proteins were removed (protein(s) only identified by site,contaminant(s),reverse hit(s))"IDsdetails<-cbind(UniProt_Name=sub(".*\\|","",IDsdetails$protein.ids), IDsdetails)
write.csv(IDsdetails,file="./03_Output_files/IDs details.csv")
colnames(data)<- sub("iBAQ.","",colnames(data))
data[,1]<- sub(".*\\|","",data[,1])
metadata<- read.csv(file = "./01_Source_files/metadata.csv")
colnames(metadata)<-tolower(colnames(metadata))Romics_object creation
The data and metadata were placed in an romics_object, the sample names were retrieved from the metadata, the condition will be use for the coloring of the Figures.
romics_proteins<- romicsCreateObject(data, metadata,main_factor = "Condition")Data cleaning and normalization
The zeros were replaced by missing values
romics_proteins<-romicsZeroToMissing(romics_proteins)The proteins to be conserved for quantification were selected to contain at least 60% of complete value for a given condition (2/3 sample of a given condition at least), the overall missingness was evaluated after filtering.
romics_proteins<-romicsFilterMissing(romics_proteins,percentage_completeness = 60)## [1] "238 rows were removed for the data"
## [1] "Based on the minimum completeness set at 60%"
## [1] "at least the following number of sample(s) containing data was required:"
## A_G0 A_G1 WT_G0
## 2 2 2print(paste0(nrow(romics_proteins$data),"/", nrow(romics_proteins$original_data)," proteins remained after filtering", " (",round(nrow(romics_proteins$data)/nrow(romics_proteins$original_data)*100,2),"%)."))## [1] "2151/2389 proteins remained after filtering (90.04%)."The data was log2 transformed, the distriution boxplot were then plotted
romics_proteins<-log2transform(romics_proteins)
distribBoxplot(romics_proteins)
As the same quantity of protein was labelled for each sample, the expectation is that the distribution of the protein abundance is centered, therefore a median centering was performed prior to plot again the distribution boxplots.
romics_proteins<-medianCenterSample(romics_proteins)
distribBoxplot(romics_proteins)
Data imputation
For some of the subsequent statistics imputations are required, we performed an imputation by assuming that the “non-detected” proteins were either low abundance or missing using the method developped by Tyranova et al. (PMID: 27348712). The gray distribution is the data distribution, the yellow distribution is the one for the random values used for imputation.
imputeMissingEval(romics_proteins,nb_stdev = 2,width_stdev = 0.5, bin=1)
romics_proteins<-imputeMissing(romics_proteins,nb_stdev = 2,width_stdev = 0.5)The PCA grouping were checked after imputation
indPCAplot(romics_proteins, plotType = "percentage")
indPCAplot(romics_proteins, plotType = "individual",Xcomp=1,Ycomp =2,label = F)
indPCAplot(romics_proteins, plotType = "individual",Xcomp=1,Ycomp =3,label = F)
indPCA3D(romics_proteins)We will extract the contributions of the proteins to the 3 first components
PCA_results<-romicsPCA(romics_proteins)
PCA_var_coord<-data.frame(PCA_results$var$coord[,1:3])
colnames(PCA_var_coord)<-c("PC1","PC2","PC3")
ggplot(PCA_var_coord, aes(x=PCA_var_coord[,1], y=PCA_var_coord[,2]))+
geom_point(size = 3,alpha=I(0.5)) +
xlab("PC1")+
ylab("PC2")+
ggtitle("Principal component analysis protein contributions")+
theme_ROP()
ggplot(PCA_var_coord, aes(x=PCA_var_coord[,2], y=PCA_var_coord[,3]))+
geom_point(size = 3,alpha=I(0.5)) +
xlab("PC2")+
ylab("PC3")+
ggtitle("Principal component analysis protein contributions")+
theme_ROP()
We’ve extracted the top10% proteins contributing the most to each PCA axis
tenpercentproteins<-round(nrow(PCA_var_coord)*10/100,digits = 0)
print("Proteins contributions to PC1")## [1] "Proteins contributions to PC1" top10percentPC1<-PCA_var_coord[1]
top10percentPC1_names<-rownames(top10percentPC1)
top10percentPC1<-as.numeric(t(top10percentPC1))
names(top10percentPC1)<-gsub(";.*","",top10percentPC1_names)
top10percentPC1<-abs(top10percentPC1)
top10percentPC1<-top10percentPC1[order(top10percentPC1,decreasing = T)]
head(data.frame(abs_contrib=top10percentPC1),10)## abs_contrib
## Q9QYB1 0.9933345
## Q3TGF2 0.9928248
## Q9CQR2 0.9808388
## P14733 0.9787970
## P49710 0.9780563
## O89053 0.9780260
## P0DOV1 0.9724403
## E9Q4Q2 0.9724311
## Q60591 0.9714436
## A0A5F8MPB5 0.9705373 top10percentPC1<-names(top10percentPC1[1:tenpercentproteins])
print("Proteins contributions to PC2")## [1] "Proteins contributions to PC2" top10percentPC2<-PCA_var_coord[2]
top10percentPC2_names<-rownames(top10percentPC2)
top10percentPC2<-as.numeric(t(top10percentPC2))
names(top10percentPC2)<-gsub(";.*","",top10percentPC2_names)
top10percentPC2<-abs(top10percentPC2)
top10percentPC2<-top10percentPC2[order(top10percentPC2,decreasing = T)]
head(data.frame(abs_contrib=top10percentPC2),10)## abs_contrib
## O09159 0.9799565
## Q3TLP8 0.9766476
## P54116 0.9653471
## E9Q616 0.9625872
## G3X8T3 0.9550060
## Q99JI4 0.9526751
## A0A0U1RP68 0.9521342
## P08226 0.9457766
## P10605 0.9391953
## P09528 0.9380859 top10percentPC2<-names(top10percentPC2[1:tenpercentproteins])
print("Proteins contributions to PC3")## [1] "Proteins contributions to PC3" top10percentPC3<-PCA_var_coord[3]
top10percentPC3_names<-rownames(top10percentPC3)
top10percentPC3<-as.numeric(t(top10percentPC3))
names(top10percentPC3)<-gsub(";.*","",top10percentPC3_names)
top10percentPC3<-abs(top10percentPC3)
top10percentPC3<-top10percentPC3[order(top10percentPC3,decreasing = T)]
head(data.frame(abs_contrib=top10percentPC3),10)## abs_contrib
## Q91WG2 0.9575724
## P48771 0.9276357
## Q99KP3 0.9206637
## Q6GT24 0.9137083
## Q9CPV4 0.9112642
## B1ATL6 0.9044273
## O70570 0.8900107
## Q78IK2 0.8737668
## Q8VE37 0.8547488
## Q9DBT9 0.8477441 top10percentPC3<-names(top10percentPC3[1:tenpercentproteins])
universe<-gsub(";.*","",rownames(PCA_var_coord))
write.csv(top10percentPC1,"./03_Output_files/top10percentPC1.csv")
write.csv(top10percentPC2,"./03_Output_files/top10percentPC2.csv")
write.csv(top10percentPC3,"./03_Output_files/top10percentPC3.csv")Now let’s perform enrichment analysis to evaluate the function participating the most to these separations
PC1_top10_enrich <- cbind(Type="GO top10% PC1", UniProt_GO_Fisher(top10percentPC1,universe))## [1] "Your <query> contained 0 UniProt_IDs and 215 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."PC2_top10_enrich <- cbind(Type="GO top10% PC2", UniProt_GO_Fisher(top10percentPC2,universe))## [1] "Your <query> contained 0 UniProt_IDs and 214 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."PC3_top10_enrich <- cbind(Type="GO top10% PC3", UniProt_GO_Fisher(top10percentPC3,universe))## [1] "Your <query> contained 0 UniProt_IDs and 215 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."PC1_top10_enrich_KEGG <- cbind(Type="KEGG top10% PC1", UniProt_KEGG_Fisher(top10percentPC1,universe))## [1] "Your <query> contained 0 UniProt_IDs and 215 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."PC2_top10_enrich_KEGG <- cbind(Type="KEGG top10% PC2", UniProt_KEGG_Fisher(top10percentPC2,universe))## [1] "Your <query> contained 0 UniProt_IDs and 214 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."PC3_top10_enrich_KEGG <- cbind(Type="KEGG top10% PC3", UniProt_KEGG_Fisher(top10percentPC3,universe))## [1] "Your <query> contained 0 UniProt_IDs and 215 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."enriched_top10_percent<-rbind(PC1_top10_enrich,PC2_top10_enrich,PC3_top10_enrich,PC1_top10_enrich_KEGG ,PC2_top10_enrich_KEGG ,PC3_top10_enrich_KEGG )
enriched_top10_percent<-enriched_top10_percent[enriched_top10_percent$pval<0.05&enriched_top10_percent$fold_change>1,]
head(enriched_top10_percent,10)## Type Ontology_type Ontology_accession
## 419 GO top10% PC1 GO 3690
## 294 GO top10% PC1 GO 17124
## 2 GO top10% PC1 GO 30027
## 835 GO top10% PC1 GO 30261
## 838 GO top10% PC1 GO 31936
## 839 GO top10% PC1 GO 45910
## 841 GO top10% PC1 GO 16584
## 611 GO top10% PC1 GO 6355
## 63 GO top10% PC1 GO 5634
## 601 GO top10% PC1 GO 6974
## Ongology_description Count_query Count_universe
## 419 double-stranded DNA binding 10/215 19/2150
## 294 SH3 domain binding 11/215 25/2150
## 2 lamellipodium 14/215 48/2150
## 835 chromosome condensation 5/215 6/2150
## 838 negative regulation of chromatin silencing 5/215 6/2150
## 839 negative regulation of DNA recombination 5/215 6/2150
## 841 nucleosome positioning 5/215 6/2150
## 611 regulation of transcription, DNA-templated 10/215 29/2150
## 63 nucleus 64/215 442/2150
## 601 cellular response to DNA damage stimulus 7/215 15/2150
## %_query %_universe pval adjpval fold_change
## 419 4.651163 0.8837209 0.0001331752 0.2291944 5.263158
## 294 5.116279 1.1627907 0.0002105714 0.3623933 4.400000
## 2 6.511628 2.2325581 0.0009562050 1.0000000 2.916667
## 835 2.325581 0.2790698 0.0017322657 1.0000000 8.333333
## 838 2.325581 0.2790698 0.0017322657 1.0000000 8.333333
## 839 2.325581 0.2790698 0.0017322657 1.0000000 8.333333
## 841 2.325581 0.2790698 0.0017322657 1.0000000 8.333333
## 611 4.651163 1.3488372 0.0018712602 1.0000000 3.448276
## 63 29.767442 20.5581395 0.0022403608 1.0000000 1.447964
## 601 3.255814 0.6976744 0.0024141873 1.0000000 4.666667
## Proteins_in_query
## 419 IFI5B_MOUSE;PURB_MOUSE;H12_MOUSE;LMNB1_MOUSE;H15_MOUSE;H13_MOUSE;H14_MOUSE;H11_MOUSE;IFIX_MOUSE;IFI4_MOUSE
## 294 PTN6_MOUSE;KHDR1_MOUSE;VASP_MOUSE;LYN_MOUSE;HCLS1_MOUSE;ELMO1_MOUSE;CY24A_MOUSE;WASF2_MOUSE;WASP_MOUSE;WIPF1_MOUSE;F8WJB9_MOUSE
## 2 BRK1_MOUSE;SHOT1_MOUSE;COR1B_MOUSE;VASP_MOUSE;PDIA1_MOUSE;SNX2_MOUSE;PABP1_MOUSE;HCLS1_MOUSE;ACTB_MOUSE;TWF2_MOUSE;DBNL_MOUSE;ACTA_MOUSE;COR1A_MOUSE;WASF2_MOUSE
## 835 H12_MOUSE;H15_MOUSE;H13_MOUSE;H14_MOUSE;H11_MOUSE
## 838 H12_MOUSE;H15_MOUSE;H13_MOUSE;H14_MOUSE;H11_MOUSE
## 839 H12_MOUSE;H15_MOUSE;H13_MOUSE;H14_MOUSE;H11_MOUSE
## 841 H12_MOUSE;H15_MOUSE;H13_MOUSE;H14_MOUSE;H11_MOUSE
## 611 SFPQ_MOUSE;H12_MOUSE;H15_MOUSE;LAP2B_MOUSE;H13_MOUSE;H14_MOUSE;HMGA1_MOUSE;H11_MOUSE;Q5SUT0_MOUSE;Z4YKB8_MOUSE
## 63 4EBP1_MOUSE;RGS10_MOUSE;ROA2_MOUSE;SF3A1_MOUSE;MATR3_MOUSE;PTN6_MOUSE;PI42A_MOUSE;PP2AA_MOUSE;LBR_MOUSE;PP1R7_MOUSE;IFI5B_MOUSE;PGAM2_MOUSE;DX39B_MOUSE;KHDR1_MOUSE;PURB_MOUSE;SFPQ_MOUSE;PAK2_MOUSE;RL12_MOUSE;SAMH1_MOUSE;HG2A_MOUSE;KPCB_MOUSE;NFAC2_MOUSE;H12_MOUSE;NOP58_MOUSE;PABP1_MOUSE;FUMH_MOUSE;KCC1A_MOUSE;PSA1_MOUSE;LMNB1_MOUSE;H15_MOUSE;ARPC4_MOUSE;G6PD1_MOUSE;LAP2B_MOUSE;LYN_MOUSE;FYB1_MOUSE;H13_MOUSE;H14_MOUSE;LYRIC_MOUSE;GNAI2_MOUSE;HMGA1_MOUSE;H11_MOUSE;H2B1B_MOUSE;IFIX_MOUSE;HCLS1_MOUSE;H2AX_MOUSE;IFI4_MOUSE;ACTB_MOUSE;DJB11_MOUSE;CY24A_MOUSE;1433B_MOUSE;F120A_MOUSE;CAND1_MOUSE;CLIC4_MOUSE;DNJA2_MOUSE;BASP1_MOUSE;SKP1_MOUSE;TOIP1_MOUSE;TCP4_MOUSE;WASP_MOUSE;Q62347_MOUSE;D3YW09_MOUSE;Q99K94_MOUSE;Q3UJB0_MOUSE;Z4YKB8_MOUSE
## 601 SFPQ_MOUSE;SAMH1_MOUSE;NFAC2_MOUSE;FUMH_MOUSE;LYN_MOUSE;H2AX_MOUSE;Q9DCC5_MOUSEStatistics
The means and stdev are calculated for each group
romics_proteins<-romicsMean(romics_proteins)## [1] "The Statistics layer was added to your object"
## [1] "Means columns (*_mean) were added to the statistics"romics_proteins<-romicsSd(romics_proteins)## [1] "The standard deviation columns (*_sd) were added to the statistics"Some general statistics are performed (ANOVA, paired T.tests). First the ANOVA was performed
romics_proteins<-romicsANOVA(romics_proteins)## [1] "The ANOVA columns (ANOVA_p and ANOVA_padj) were added to the statistics"print(paste0(sum(romics_proteins$statistics$ANOVA_p<0.05), " proteins had an ANOVA p<0.05."))## [1] "557 proteins had an ANOVA p<0.05."the pvalue distribtion was plotted
pval<-data.frame(ids=rownames(romics_proteins$statistics), p=romics_proteins$statistics$ANOVA_p)
ggplot(pval, aes(p)) + geom_histogram(binwidth = 0.01)+theme_ROP()+ggtitle("ANOVA p frequency plot")
A heatmap depicting the proteins passing an ANOVA p<0.05 is plotted, the clusters obtained were saved in the statistics.
romicsHeatmap(romics_proteins,variable_hclust_number = 3,ANOVA_filter = "p", p=0.05,sample_hclust = F)
romics_proteins<-romicsVariableHclust(romics_proteins,clusters = 3,ANOVA_filter = "p",p= 0.05,plot = F)## [1] "The columns hclust_clusters was added to the statistics"romics_proteins<-romicsZscores(romics_proteins)## [1] "Z_score_ columns were added to the statistics"Student’s T.tests were then performed to compare specific conditions with each other.
romics_proteins<-romicsTtest(romics_proteins)## [1] "T_test columns were added to the statistics"romicsVolcano(romics_proteins)## [1] "'stat_type' was missing 't.test' were used by default"
The proteins up and down within each t.test were used to performed enrichment analysis to evaluate what functions were enriched in the different significant groups
Universe<-gsub("\\;.*","",rownames(romics_proteins$statistics))
up_ASG1_vs_ASG0 <- gsub("\\;.*","",rownames(romics_proteins$statistics)[romics_proteins$statistics$A_G1_vs_A_G0_Ttest_p<0.05&romics_proteins$statistics$`log(A_G1/A_G0)`>0])
up_WTG0_vs_ASG0 <- gsub("\\;.*","",rownames(romics_proteins$statistics)[romics_proteins$statistics$WT_G0_vs_A_G0_Ttest_p<0.05&romics_proteins$statistics$`log(WT_G0/A_G0)`>0])
up_WTG0_vs_ASG1 <- gsub("\\;.*","",rownames(romics_proteins$statistics)[romics_proteins$statistics$WT_G0_vs_A_G1_Ttest_p<0.05&romics_proteins$statistics$`log(WT_G0/A_G1)`>0])
down_ASG1_vs_ASG0 <- gsub("\\;.*","",rownames(romics_proteins$statistics)[romics_proteins$statistics$A_G1_vs_A_G0_Ttest_p<0.05&romics_proteins$statistics$`log(A_G1/A_G0)`<0])
down_WTG0_vs_ASG0 <- gsub("\\;.*","",rownames(romics_proteins$statistics)[romics_proteins$statistics$WT_G0_vs_A_G0_Ttest_p<0.05&romics_proteins$statistics$`log(WT_G0/A_G0)`<0])
down_WTG0_vs_ASG1 <- gsub("\\;.*","",rownames(romics_proteins$statistics)[romics_proteins$statistics$WT_G0_vs_A_G1_Ttest_p<0.05&romics_proteins$statistics$`log(WT_G0/A_G1)`<0])
up_ASG1_vs_ASG0_GO <- cbind(Enriched_in="up_ASG1_vs_ASG0", UniProt_GO_Fisher(up_ASG1_vs_ASG0,Universe))## [1] "Your <query> contained 0 UniProt_IDs and 38 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."up_WTG0_vs_ASG0_GO <-cbind(Enriched_in="up_WTG0_vs_ASG0", UniProt_GO_Fisher(up_WTG0_vs_ASG0,Universe))## [1] "Your <query> contained 0 UniProt_IDs and 263 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."up_WTG0_vs_ASG1_GO <-cbind(Enriched_in="up_WTG0_vs_ASG1", UniProt_GO_Fisher(up_WTG0_vs_ASG1,Universe))## [1] "Your <query> contained 0 UniProt_IDs and 352 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."down_ASG1_vs_ASG0_GO <-cbind(Enriched_in="down_ASG1_vs_ASG0", UniProt_GO_Fisher(down_ASG1_vs_ASG0,Universe))## [1] "Your <query> contained 0 UniProt_IDs and 180 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."down_WTG0_vs_ASG0_GO <-cbind(Enriched_in="down_WTG0_vs_ASG0", UniProt_GO_Fisher(down_WTG0_vs_ASG0,Universe))## [1] "Your <query> contained 0 UniProt_IDs and 72 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."down_WTG0_vs_ASG1_GO <-cbind(Enriched_in="down_WTG0_vs_ASG1", UniProt_GO_Fisher(down_WTG0_vs_ASG1,Universe))## [1] "Your <query> contained 0 UniProt_IDs and 72 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."up_ASG1_vs_ASG0_KEGG <- cbind(Enriched_in="up_ASG1_vs_ASG0", UniProt_KEGG_Fisher(up_ASG1_vs_ASG0,Universe))## [1] "Your <query> contained 0 UniProt_IDs and 38 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."up_WTG0_vs_ASG0_KEGG <-cbind(Enriched_in="up_WTG0_vs_ASG0", UniProt_KEGG_Fisher(up_WTG0_vs_ASG0,Universe))## [1] "Your <query> contained 0 UniProt_IDs and 263 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."up_WTG0_vs_ASG1_KEGG <-cbind(Enriched_in="up_WTG0_vs_ASG1", UniProt_KEGG_Fisher(up_WTG0_vs_ASG1,Universe))## [1] "Your <query> contained 0 UniProt_IDs and 352 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."down_ASG1_vs_ASG0_KEGG <-cbind(Enriched_in="down_ASG1_vs_ASG0", UniProt_KEGG_Fisher(down_ASG1_vs_ASG0,Universe))## [1] "Your <query> contained 0 UniProt_IDs and 180 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."down_WTG0_vs_ASG0_KEGG <-cbind(Enriched_in="down_WTG0_vs_ASG0", UniProt_KEGG_Fisher(down_WTG0_vs_ASG0,Universe))## [1] "Your <query> contained 0 UniProt_IDs and 72 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."down_WTG0_vs_ASG1_KEGG <-cbind(Enriched_in="down_WTG0_vs_ASG1", UniProt_KEGG_Fisher(down_WTG0_vs_ASG1,Universe))## [1] "Your <query> contained 0 UniProt_IDs and 72 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."Enrichments_Ttests<-rbind(up_ASG1_vs_ASG0_GO,up_ASG1_vs_ASG0_KEGG,up_WTG0_vs_ASG0_GO,up_WTG0_vs_ASG0_KEGG,up_WTG0_vs_ASG1_GO,up_WTG0_vs_ASG1_KEGG,down_ASG1_vs_ASG0_GO,down_ASG1_vs_ASG0_KEGG,down_WTG0_vs_ASG0_GO,down_WTG0_vs_ASG0_KEGG,down_WTG0_vs_ASG1_GO,down_WTG0_vs_ASG1_KEGG)
Enrichments_Ttests <- Enrichments_Ttests[Enrichments_Ttests$pval<0.1& Enrichments_Ttests>1,]
write.csv(up_ASG1_vs_ASG0,file="./03_Output_files/up_ASG1_vs_ASG0.csv")
write.csv(up_WTG0_vs_ASG0,file="./03_Output_files/up_WTG0_vs_ASG0.csv")
write.csv(up_WTG0_vs_ASG1,file="./03_Output_files/up_WTG0_vs_ASG1.csv")
write.csv(down_ASG1_vs_ASG0,file="./03_Output_files/down_ASG1_vs_ASG0.csv")
write.csv(down_WTG0_vs_ASG0,file="./03_Output_files/down_WTG0_vs_ASG0.csv")
write.csv(down_WTG0_vs_ASG1,file="./03_Output_files/down_WTG0_vs_ASG1.csv")
head(Enrichments_Ttests,10)## Enriched_in Ontology_type Ontology_accession
## 134 up_ASG1_vs_ASG0 GO 51017
## 280 up_ASG1_vs_ASG0 GO 8092
## 13 up_ASG1_vs_ASG0 GO 42130
## 59 up_ASG1_vs_ASG0 GO 7517
## 169 up_ASG1_vs_ASG0 GO 30388
## 171 up_ASG1_vs_ASG0 GO 6000
## 54 up_ASG1_vs_ASG0 GO 8157
## 32 up_ASG1_vs_ASG0 GO 42802
## 234 up_ASG1_vs_ASG0 GO 51764
## 187 up_ASG1_vs_ASG0 GO 7399
## Ongology_description Count_query Count_universe
## 134 actin filament bundle assembly 4/38 15/2150
## 280 cytoskeletal protein binding 3/38 11/2150
## 13 negative regulation of T cell proliferation 2/38 4/2150
## 59 muscle organ development 2/38 4/2150
## 169 fructose 1,6-bisphosphate metabolic process 2/38 4/2150
## 171 fructose metabolic process 2/38 4/2150
## 54 protein phosphatase 1 binding 2/38 5/2150
## 32 identical protein binding 10/38 237/2150
## 234 actin crosslink formation 2/38 6/2150
## 187 nervous system development 2/38 10/2150
## %_query %_universe pval adjpval fold_change
## 134 10.526316 0.6976744 0.0002485607 0.0949502 15.087719
## 280 7.894737 0.5116279 0.0015405162 0.5884772 15.430622
## 13 5.263158 0.1860465 0.0042135173 1.0000000 28.289474
## 59 5.263158 0.1860465 0.0042135173 1.0000000 28.289474
## 169 5.263158 0.1860465 0.0042135173 1.0000000 28.289474
## 171 5.263158 0.1860465 0.0042135173 1.0000000 28.289474
## 54 5.263158 0.2325581 0.0058344625 1.0000000 22.631579
## 32 26.315789 11.0232558 0.0074746830 1.0000000 2.387297
## 234 5.263158 0.2790698 0.0076943647 1.0000000 18.859649
## 187 5.263158 0.4651163 0.0173597409 1.0000000 11.315789
## Proteins_in_query
## 134 PAWR_MOUSE;DPYL3_MOUSE;A1BN54_MOUSE;Q8VCQ8_MOUSE
## 280 DESM_MOUSE;ALDOB_MOUSE;E9Q805_MOUSE
## 13 UTER_MOUSE;PAWR_MOUSE
## 59 LMNA_MOUSE;DESM_MOUSE
## 169 F16P1_MOUSE;ALDOB_MOUSE
## 171 F16P1_MOUSE;ALDOB_MOUSE
## 54 LMNA_MOUSE;PAWR_MOUSE
## 32 RIDA_MOUSE;LMNA_MOUSE;F16P1_MOUSE;GLNA_MOUSE;DPYL3_MOUSE;DHSO_MOUSE;DESM_MOUSE;DPP4_MOUSE;ALDOB_MOUSE;E9Q8N8_MOUSE
## 234 DPYL3_MOUSE;A1BN54_MOUSE
## 187 NEST_MOUSE;DPYL3_MOUSEThe same was done for the different Clusters of the heatmap
Clust1<-gsub("\\;.*","",rownames(romics_proteins$statistics)[!is.na(romics_proteins$statistics$hclust_clusters)&romics_proteins$statistics$hclust_clusters==1])
Clust2<-gsub("\\;.*","",rownames(romics_proteins$statistics)[!is.na(romics_proteins$statistics$hclust_clusters)&romics_proteins$statistics$hclust_clusters==2])
Clust3<-gsub("\\;.*","",rownames(romics_proteins$statistics)[!is.na(romics_proteins$statistics$hclust_clusters)&romics_proteins$statistics$hclust_clusters==3])
Clust1_GO<-cbind(Cluster=1, UniProt_GO_Fisher(Clust1,Universe))## [1] "Your <query> contained 0 UniProt_IDs and 119 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."Clust2_GO<-cbind(Cluster=2, UniProt_GO_Fisher(Clust2,Universe))## [1] "Your <query> contained 0 UniProt_IDs and 341 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."Clust3_GO<-cbind(Cluster=3, UniProt_GO_Fisher(Clust3,Universe))## [1] "Your <query> contained 0 UniProt_IDs and 96 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."Clust1_KEGG<-cbind(Cluster=1, UniProt_KEGG_Fisher(Clust1,Universe))## [1] "Your <query> contained 0 UniProt_IDs and 119 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."Clust2_KEGG<-cbind(Cluster=2, UniProt_KEGG_Fisher(Clust2,Universe))## [1] "Your <query> contained 0 UniProt_IDs and 341 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."Clust3_KEGG<-cbind(Cluster=3, UniProt_KEGG_Fisher(Clust3,Universe))## [1] "Your <query> contained 0 UniProt_IDs and 96 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."Enrichment_clusters<-rbind(Clust1_GO,Clust2_GO,Clust3_GO,Clust1_KEGG,Clust2_KEGG,Clust3_KEGG)
Enrichment_clusters<-Enrichment_clusters[Enrichment_clusters$pval<0.1 & Enrichment_clusters$fold_change>1,]
write.csv(Clust1,file="./03_Output_files/Clust1.csv")
write.csv(Clust2,file="./03_Output_files/Clust2.csv")
write.csv(Clust3,file="./03_Output_files/Clust3.csv")
head(Enrichment_clusters,10)## Cluster Ontology_type Ontology_accession
## 7 1 GO 5615
## 236 1 GO 36094
## 9 1 GO 5764
## 418 1 GO 8199
## 51 1 GO 5509
## 687 1 GO 6826
## 6 1 GO 5576
## 39 1 GO 6879
## 3 1 GO 62023
## 179 1 GO 31232
## Ongology_description Count_query
## 7 extracellular space 26/119
## 236 small molecule binding 5/119
## 9 lysosome 10/119
## 418 ferric iron binding 4/119
## 51 calcium ion binding 14/119
## 687 iron ion transport 4/119
## 6 extracellular region 11/119
## 39 cellular iron ion homeostasis 5/119
## 3 collagen-containing extracellular matrix 8/119
## 179 extrinsic component of external side of plasma membrane 3/119
## Count_universe %_query %_universe pval adjpval fold_change
## 7 120/2150 21.848739 5.5813953 9.060060e-09 9.775804e-06 3.914566
## 236 6/2150 4.201681 0.2790698 1.305574e-04 1.408715e-01 15.056022
## 9 44/2150 8.403361 2.0465116 3.611612e-04 3.896929e-01 4.106188
## 418 4/2150 3.361345 0.1860465 4.269432e-04 4.606718e-01 18.067227
## 51 86/2150 11.764706 4.0000000 5.241995e-04 5.656112e-01 2.941176
## 687 5/2150 3.361345 0.2325581 7.374749e-04 7.957354e-01 14.453782
## 6 59/2150 9.243697 2.7441860 8.018514e-04 8.651977e-01 3.368466
## 39 11/2150 4.201681 0.5116279 9.979355e-04 1.000000e+00 8.212376
## 3 35/2150 6.722689 1.6279070 1.391132e-03 1.000000e+00 4.129652
## 179 3/2150 2.521008 0.1395349 2.501941e-03 1.000000e+00 18.067227
## Proteins_in_query
## 7 CATB_MOUSE;HEMO_MOUSE;MUG1_MOUSE;PLMN_MOUSE;ALBU_MOUSE;ANXA5_MOUSE;NGP_MOUSE;MUP2_MOUSE;LYZ2_MOUSE;AMBP_MOUSE;APOE_MOUSE;FBN1_MOUSE;DPYL3_MOUSE;A1AT4_MOUSE;ASAH1_MOUSE;CFAD_MOUSE;CLUS_MOUSE;S10A9_MOUSE;TRFE_MOUSE;UROM_MOUSE;VTNC_MOUSE;A0A0R4J0I1_MOUSE;F8WIP8_MOUSE;G3X9T8_MOUSE;Q9CPN9_MOUSE;D3YYD0_MOUSE
## 236 MUP20_MOUSE;MUP2_MOUSE;A0A0A6YW77_MOUSE;A2BIN1_MOUSE;Q58EV3_MOUSE
## 9 CATB_MOUSE;LMBD1_MOUSE;SAP3_MOUSE;ST1C2_MOUSE;LAMP1_MOUSE;APOE_MOUSE;CAN2_MOUSE;ASAH1_MOUSE;VPS36_MOUSE;Q9JHF5_MOUSE
## 418 MIOX_MOUSE;FRIH_MOUSE;TRFE_MOUSE;Q9CPX4_MOUSE
## 51 SCMC1_MOUSE;S10AA_MOUSE;ANXA5_MOUSE;PAMR1_MOUSE;ANXA3_MOUSE;FBN1_MOUSE;CAN2_MOUSE;S10A9_MOUSE;UROM_MOUSE;A0A1B0GR19_MOUSE;A0A494BBD8_MOUSE;A0A1L1SV25_MOUSE;D3YV37_MOUSE;A1BN54_MOUSE
## 687 FRIH_MOUSE;TRFE_MOUSE;G3X9T8_MOUSE;Q9CPX4_MOUSE
## 6 CATB_MOUSE;PLMN_MOUSE;MUP20_MOUSE;ALBU_MOUSE;PAMR1_MOUSE;APOE_MOUSE;FBN1_MOUSE;A1AT4_MOUSE;FRIH_MOUSE;DPP4_MOUSE;TRFE_MOUSE
## 39 HEMO_MOUSE;TRFE_MOUSE;VA0D1_MOUSE;G3X9T8_MOUSE;Q9CPX4_MOUSE
## 3 CATB_MOUSE;S10AA_MOUSE;PLMN_MOUSE;ANXA5_MOUSE;AMBP_MOUSE;ANXA3_MOUSE;FBN1_MOUSE;VTNC_MOUSE
## 179 PLMN_MOUSE;APOE_MOUSE;TRFE_MOUSEEulerr Diagrams and enrichment
To visualize the proteins that were significant in different comparison a proportional Euler/Venn diagram were plotted the proteins unique to each comparion were used to performed enrichment analysis
#First lets create the lists
ASG1_vs_ASG0 <- gsub("\\;.*","",rownames(romics_proteins$statistics)[romics_proteins$statistics$A_G1_vs_A_G0_Ttest_p<0.05])
WTG0_vs_ASG0 <- gsub("\\;.*","",rownames(romics_proteins$statistics)[romics_proteins$statistics$WT_G0_vs_A_G0_Ttest_p<0.05])
WTG0_vs_ASG1 <- gsub("\\;.*","",rownames(romics_proteins$statistics)[romics_proteins$statistics$WT_G0_vs_A_G1_Ttest_p<0.05])
combined_names<- unique(c(ASG1_vs_ASG0,WTG0_vs_ASG0,WTG0_vs_ASG1))
Venn_table<-data.frame(ASG1_vs_ASG0=(combined_names %in% ASG1_vs_ASG0),WTG0_vs_ASG0=(combined_names %in% WTG0_vs_ASG0),WTG0_vs_ASG1=(combined_names %in% WTG0_vs_ASG1))
rownames(Venn_table)<-combined_names
plot(euler(Venn_table), quantities = TRUE,fills = c("#00646d","#b71e5d","#dba027"))
ASG1_vs_ASG0_unique<-rownames(Venn_table)[rowSums(Venn_table)==1 & Venn_table$ASG1_vs_ASG0==1]
WTG0_vs_ASG0_unique<-rownames(Venn_table)[rowSums(Venn_table)==1 & Venn_table$WTG0_vs_ASG0==1]
WTG0_vs_ASG1_unique<-rownames(Venn_table)[rowSums(Venn_table)==1 & Venn_table$WTG0_vs_ASG1==1]
shared_in_3_comparisons<-rownames(Venn_table)[rowSums(Venn_table)==3]
ASG1_vs_ASG0_unique_GO<-cbind(Enrichment_for="ASG1_vs_ASG0_unique", UniProt_GO_Fisher(ASG1_vs_ASG0_unique,Universe))## [1] "Your <query> contained 0 UniProt_IDs and 132 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."WTG0_vs_ASG0_unique_GO<-cbind(Enrichment_for="WTG0_vs_ASG0_unique", UniProt_GO_Fisher(WTG0_vs_ASG0_unique,Universe))## [1] "Your <query> contained 0 UniProt_IDs and 123 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."WTG0_vs_ASG1_unique_GO<-cbind(Enrichment_for="WTG0_vs_ASG1_unique", UniProt_GO_Fisher(WTG0_vs_ASG1_unique,Universe))## [1] "Your <query> contained 0 UniProt_IDs and 176 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."ASG1_vs_ASG0_unique_KEGG<-cbind(Enrichment_for="ASG1_vs_ASG0_unique", UniProt_KEGG_Fisher(ASG1_vs_ASG0_unique,Universe))## [1] "Your <query> contained 0 UniProt_IDs and 132 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."WTG0_vs_ASG0_unique_KEGG<-cbind(Enrichment_for="WTG0_vs_ASG0_unique", UniProt_KEGG_Fisher(WTG0_vs_ASG0_unique,Universe))## [1] "Your <query> contained 0 UniProt_IDs and 123 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."WTG0_vs_ASG1_unique_KEGG<-cbind(Enrichment_for="WTG0_vs_ASG1_unique", UniProt_KEGG_Fisher(WTG0_vs_ASG1_unique,Universe))## [1] "Your <query> contained 0 UniProt_IDs and 176 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."Venn_unique_enrichments<-rbind(ASG1_vs_ASG0_unique_GO,ASG1_vs_ASG0_unique_KEGG,WTG0_vs_ASG0_unique_GO,WTG0_vs_ASG0_unique_KEGG,WTG0_vs_ASG1_unique_GO,WTG0_vs_ASG1_unique_KEGG)
Venn_unique_enrichments<-Venn_unique_enrichments[Venn_unique_enrichments$pval<0.1&Venn_unique_enrichments$fold_change>1,]
head(Venn_unique_enrichments,10)## Enrichment_for Ontology_type Ontology_accession
## 447 ASG1_vs_ASG0_unique GO 6909
## 423 ASG1_vs_ASG0_unique GO 3756
## 154 ASG1_vs_ASG0_unique GO 42130
## 389 ASG1_vs_ASG0_unique GO 16272
## 625 ASG1_vs_ASG0_unique GO 1901741
## 126 ASG1_vs_ASG0_unique GO 43525
## 652 ASG1_vs_ASG0_unique GO 5834
## 416 ASG1_vs_ASG0_unique GO 42824
## 422 ASG1_vs_ASG0_unique GO 15037
## 166 ASG1_vs_ASG0_unique GO 15629
## Ongology_description Count_query Count_universe
## 447 phagocytosis 5/132 14/2150
## 423 protein disulfide isomerase activity 4/132 9/2150
## 154 negative regulation of T cell proliferation 3/132 4/2150
## 389 prefoldin complex 3/132 4/2150
## 625 positive regulation of myoblast fusion 3/132 4/2150
## 126 positive regulation of neuron apoptotic process 4/132 10/2150
## 652 heterotrimeric G-protein complex 4/132 10/2150
## 416 MHC class I peptide loading complex 3/132 5/2150
## 422 peptide disulfide oxidoreductase activity 3/132 5/2150
## 166 actin cytoskeleton 7/132 35/2150
## %_query %_universe pval adjpval fold_change
## 447 3.787879 0.6511628 0.003608339 1 5.817100
## 423 3.030303 0.4186047 0.005073723 1 7.239057
## 154 2.272727 0.1860465 0.005570421 1 12.215909
## 389 2.272727 0.1860465 0.005570421 1 12.215909
## 625 2.272727 0.1860465 0.005570421 1 12.215909
## 126 3.030303 0.4651163 0.006788911 1 6.515152
## 652 3.030303 0.4651163 0.006788911 1 6.515152
## 416 2.272727 0.2325581 0.008538493 1 9.772727
## 422 2.272727 0.2325581 0.008538493 1 9.772727
## 166 5.303030 1.6279070 0.009064203 1 3.257576
## Proteins_in_query
## 447 RAB5A_MOUSE;NCF2_MOUSE;COR1C_MOUSE;TM9S4_MOUSE;A0A0R4J0I9_MOUSE
## 423 PDIA3_MOUSE;PDIA1_MOUSE;Q3TML0_MOUSE;E9PXX7_MOUSE
## 154 UTER_MOUSE;PAWR_MOUSE;HB2A_MOUSE
## 389 PFD2_MOUSE;PFD6_MOUSE;A0A494BBK3_MOUSE
## 625 NFAC2_MOUSE;MK14_MOUSE;EHD1_MOUSE
## 126 CDC42_MOUSE;ITA1_MOUSE;PAWR_MOUSE;NCF2_MOUSE
## 652 GBG5_MOUSE;GNAQ_MOUSE;GNAI2_MOUSE;GBG2_MOUSE
## 416 PDIA3_MOUSE;HA11_MOUSE;Q3TCU5_MOUSE
## 422 PDIA3_MOUSE;PDIA1_MOUSE;Q3TML0_MOUSE
## 166 WDR1_MOUSE;PLSL_MOUSE;RAB5A_MOUSE;PAWR_MOUSE;FYB1_MOUSE;CSRP1_MOUSE;COR1C_MOUSEwrite.csv(ASG1_vs_ASG0_unique,"./03_Output_files/ASG1_vs_ASG0_unique.csv",row.names = F)
write.csv(WTG0_vs_ASG0_unique,"./03_Output_files/WTG0_vs_ASG0_unique.csv",row.names = F)
write.csv(WTG0_vs_ASG1_unique,"./03_Output_files/WTG0_vs_ASG1_unique.csv",row.names = F)
write.table(Venn_unique_enrichments,file="./03_Output_files/Venn_unique_enrichments.txt",sep = "\t",row.names = F)Data export
The data generated abd the enrichment analysis are exported as a csv file
export_stats<-romicsExportData(romics_proteins,statistics = T,missing_data = T)
export_stats<-cbind(UniProt_Name=rownames(export_stats),export_stats)
export_stats<-merge(export_stats,IDsdetails,by="UniProt_Name")
write.csv(export_stats,file= "./03_Output_files/Cell_cycle_alport_proteomics.csv")
write.table(enriched_top10_percent,file="./03_Output_files/enriched_top10_percent_PC.txt",sep="\t")
write.table(Enrichments_Ttests,file= "./03_Output_files/Enrichments_Ttests.txt",sep="\t")
write.table(Enrichment_clusters,file= "./03_Output_files/Enrichment_clusters.txt",sep="\t")